






import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Union[str, Any]:
							_lowercase							:   str 							=   test_file.split(os.path.sep				)
							if components[0:2] != ["tests", "models"]:
														raise ValueError(
														    '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
														    F"""{test_file} instead."""				)
							_lowercase							:   int 							=   components[-1]
							if not test_fn.endswith('py'				):
														raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""				)
							if not test_fn.startswith('test_modeling_'				):
														raise ValueError(
														    F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""				)

							_lowercase							:   int 							=   components[:-1] + [test_fn.replace('.py'     ,     ''				)]
							_lowercase							:   Union[str, Any] 							=   '.'.join(SCREAMING_SNAKE_CASE				)

							return test_module_path






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    int:
							_lowercase							:   Tuple 							=   get_module_path(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Union[str, Any] 							=   importlib.import_module(SCREAMING_SNAKE_CASE				)

							return test_module






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Any:
							_lowercase							:   Union[str, Any] 							=   []
							_lowercase							:   Tuple 							=   get_test_module(SCREAMING_SNAKE_CASE				)
							for attr in dir(SCREAMING_SNAKE_CASE				):
														if attr.endswith('ModelTester'				):
																					tester_classes.append(getattr(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)				)

    # sort with class names
							return sorted(SCREAMING_SNAKE_CASE     ,     key=lambda SCREAMING_SNAKE_CASE				: x.__name__				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Tuple:
							_lowercase							:   str 							=   []
							_lowercase							:   Dict 							=   get_test_module(SCREAMING_SNAKE_CASE				)
							for attr in dir(SCREAMING_SNAKE_CASE				):
														_lowercase							:   Optional[Any] 							=   getattr(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
														# `all_model_classes` is not empty (which also excludes other special classes).
														_lowercase							:   List[str] 							=   getattr(SCREAMING_SNAKE_CASE     ,     'all_model_classes'     ,     []				)
														if len(SCREAMING_SNAKE_CASE				) > 0:
																					test_classes.append(SCREAMING_SNAKE_CASE				)

    # sort with class names
							return sorted(SCREAMING_SNAKE_CASE     ,     key=lambda SCREAMING_SNAKE_CASE				: x.__name__				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    List[str]:
							_lowercase							:   Union[str, Any] 							=   get_test_classes(SCREAMING_SNAKE_CASE				)
							_lowercase							:   int 							=   set()
							for test_class in test_classes:
														model_classes.update(test_class.all_model_classes				)

							# sort with class names
							return sorted(SCREAMING_SNAKE_CASE     ,     key=lambda SCREAMING_SNAKE_CASE				: x.__name__				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Optional[int]:
							_lowercase							:   List[Any] 							=   test_class()
							if hasattr(SCREAMING_SNAKE_CASE     ,     'setUp'				):
														test.setUp()

							_lowercase							:   int 							=   None
							if hasattr(SCREAMING_SNAKE_CASE     ,     'model_tester'				):
														# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
														if test.model_tester is not None:
																					_lowercase							:   List[Any] 							=   test.model_tester.__class__

							return model_tester






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    int:
							_lowercase							:   List[str] 							=   get_test_classes(SCREAMING_SNAKE_CASE				)

							_lowercase							:   List[str] 							=   []
							for test_class in test_classes:
														if model_class in test_class.all_model_classes:
																					target_test_classes.append(SCREAMING_SNAKE_CASE				)

    # sort with class names
							return sorted(SCREAMING_SNAKE_CASE     ,     key=lambda SCREAMING_SNAKE_CASE				: x.__name__				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Any:
							_lowercase							:   Optional[Any] 							=   get_test_classes_for_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

							_lowercase							:   Optional[Any] 							=   []
							for test_class in test_classes:
														_lowercase							:   List[str] 							=   get_model_tester_from_test_class(SCREAMING_SNAKE_CASE				)
														if tester_class is not None:
																					tester_classes.append(SCREAMING_SNAKE_CASE				)

    # sort with class names
							return sorted(SCREAMING_SNAKE_CASE     ,     key=lambda SCREAMING_SNAKE_CASE				: x.__name__				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    List[str]:
							_lowercase							:   Dict 							=   get_test_classes(SCREAMING_SNAKE_CASE				)
							_lowercase							:   int 							=   {test_class: get_model_tester_from_test_class(SCREAMING_SNAKE_CASE				) for test_class in test_classes}
							return test_tester_mapping






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Optional[int]:
							_lowercase							:   List[Any] 							=   get_model_classes(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Optional[Any] 							=   {
							    model_class: get_test_classes_for_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				) for model_class in model_classes
							}
							return model_test_mapping






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    str:
							_lowercase							:   int 							=   get_model_classes(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Any 							=   {
							    model_class: get_tester_classes_for_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				) for model_class in model_classes
							}
							return model_to_tester_mapping






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Optional[int]:
							if isinstance(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				):
														return o
							elif isinstance(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				):
														return o.__name__
							elif isinstance(SCREAMING_SNAKE_CASE     ,     (list, tuple)				):
														return [to_json(SCREAMING_SNAKE_CASE				) for x in o]
							elif isinstance(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				):
														return {to_json(SCREAMING_SNAKE_CASE				): to_json(SCREAMING_SNAKE_CASE				) for k, v in o.items()}
							else:
														return o












"""simple docstring"""





import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer






@require_tf
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Dict   )	->						str:
      self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		len(__SCREAMING_SNAKE_CASE   )   )
      for a, b in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        self.assertAlmostEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		delta=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						int:
      lowerCamelCase_       	=		GradientAccumulator()
      accumulator([tf.constant([1.0, 2.0]   )]   )
      accumulator([tf.constant([-2.0, 1.0]   )]   )
      accumulator([tf.constant([-1.0, 2.0]   )]   )
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        accumulator([tf.constant([1.0, 1.0]   ), tf.constant([2.0, 2.0]   )]   )
      self.assertEqual(accumulator.step			,		3   )
      self.assertEqual(len(accumulator.gradients   )			,		1   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[-2.0, 5.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[0.0, 0.0]			,		tol=1e-2   )






    def   UpperCamelCase			(			self	:				str   )	->						Any:
      lowerCamelCase_       	=		None
      ops.enable_eager_execution_internal()
      lowerCamelCase_       	=		tf.config.list_physical_devices('CPU'   )
      if len(__SCREAMING_SNAKE_CASE   ) == 1:
        tf.config.set_logical_device_configuration(
            physical_devices[0]			,		[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]   )
      lowerCamelCase_       	=		tf.config.list_logical_devices(device_type='CPU'   )
      lowerCamelCase_       	=		tf.distribute.MirroredStrategy(devices=devices[:2]   )

      with strategy.scope():
        lowerCamelCase_       	=		GradientAccumulator()
        lowerCamelCase_       	=		tf.Variable([4.0, 3.0]   )
        lowerCamelCase_	,      lowerCamelCase_       	=		create_optimizer(5e-5			,		10			,		5   )
        lowerCamelCase_       	=		tf.Variable([0.0, 0.0]			,		trainable=__SCREAMING_SNAKE_CASE   )

      def accumulate_on_replica(__SCREAMING_SNAKE_CASE	:				Union[str, Any]   ):
        accumulator([gradient]   )

      def apply_on_replica():
        optimizer.apply_gradients(list(zip(accumulator.gradients			,		[variable]   )   )   )

      @tf.function
      def accumulate(__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any   ):
        with strategy.scope():
          lowerCamelCase_       	=		strategy.experimental_local_results(__SCREAMING_SNAKE_CASE   )
          local_variables[0].assign(__SCREAMING_SNAKE_CASE   )
          local_variables[1].assign(__SCREAMING_SNAKE_CASE   )
          strategy.run(__SCREAMING_SNAKE_CASE			,		args=(gradient_placeholder,)   )

      @tf.function
      def apply_grad():
        with strategy.scope():
          strategy.run(__SCREAMING_SNAKE_CASE   )

      def _check_local_values(__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				List[str]   ):
        lowerCamelCase_       	=		strategy.experimental_local_results(accumulator._gradients[0]   )
        self.assertListAlmostEqual(values[0].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )
        self.assertListAlmostEqual(values[1].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )

      accumulate([1.0, 2.0]			,		[-1.0, 1.0]   )
      accumulate([3.0, -1.0]			,		[-1.0, -1.0]   )
      accumulate([-2.0, 2.0]			,		[3.0, -2.0]   )
      self.assertEqual(accumulator.step			,		3   )
      _check_local_values([2.0, 3.0]			,		[1.0, -2.0]   )
      apply_grad()
      self.assertListAlmostEqual(variable.value()			,		[4.0, 3.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      _check_local_values([0.0, 0.0]			,		[0.0, 0.0]   )




